aceComparative Accuracy and Safety of Large Language Models in Glaucoma Knowledge Assessment
Prcis: Across glaucoma MCQs, GPT-5.2, Gemini 3 Pro, and Claude Opus 4.5 were highly accurate, whereas DeepSeek-V3.2 underperformed; potentially harmful wrong answers were uncommon. Purpose: To compare the accuracy and potential patient-safety risk of four large language models (LLMs) on glaucoma multiple-choice questions (MCQs). Methods: DeepSeek-V3.2, GPT-5.2 (via ChatGPT), Gemini 3 Pro, and Claude Opus 4.5 were tested on two American Academy of Ophthalmology datasets: 129 text-only AAO ONE Network glaucoma sel…
GPT-5.2, Gemini 3 Pro and Claude Opus 4.5 reached 92.2% to 96.1% accuracy on 129 glaucoma self-assessment questions, with rare harmful errors.
The test used text-only multiple-choice questions, each queried once per model, and the harm-eligibility screen was post hoc, so results may not reflect real patient interactions.
Evidence
- Peer-reviewedJournal of Glaucoma2026-11-02
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Truvace Impact Record TRV-2026-2344, v1: “Comparative Accuracy and Safety of Large Language Models in Glaucoma Knowledge Assessment.” Truvace, 2026-11-03. /record/TRV-2026-2344 (accessed at citation time). sha256 819529d92e2bd1a1…
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